{
  "id": 12202285,
  "title": "6 Practical Ways to Fix Flaky Tests in Your CI Pipeline",
  "url": "https://urgent.news/2026/10/05/6-practical-ways-to-fix-flaky-tests-in-your-ci-pipeline",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-05T17:56:56.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/shanawaz_mohammed_d4a9ace/6-practical-ways-to-fix-flaky-tests-in-your-ci-pipeline-5d69"
  },
  "original_language": "en",
  "account": "Flaky tests are a common issue in continuous integration pipelines, causing frustration for developers and potentially introducing real failures. The root causes of flaky tests are often few in number, and addressing them systematically can significantly improve pipeline reliability. Here are six practical solutions to fix flaky tests, starting with the simplest and moving to more advanced methods.\n\nFirst, measure your test suite to identify flaky tests. Run your test suite multiple times with the same codebase and compare results. Tests that vary between runs are likely flaky. Use your CI platform or test reporting tools to track this data automatically. Prioritize fixing the flakiest tests first.\n\nNext, replace fixed sleep commands with explicit waits in automated tests, particularly those involving user interfaces or APIs. Instead of assuming something will happen within a certain time, wait for a condition to be met. This approach is more reliable than guessing how long something will take. The same technique applies to waiting for services to become available after startup.\n\nEnsure tests are independent of each other. Shared state between tests, such as using the same database row or global variables, can cause flakiness. When tests run in parallel or in different orders, they may interfere with each other. Fix this by having each test create its own isolated data and clean it up afterward. In pytest, use fixtures to manage this state.\n\nControl time, randomness, and interactions with external services in your tests. Tests that rely on external factors, like the current date or time, can become flaky. Freeze the clock in your tests or inject specific values to ensure consistency. Seed random number generators to make failures reproducible, and mock external APIs in unit tests to avoid relying on slow or unreliable third-party services.\n\nFor tests that cannot be fixed immediately, quarantine them from the main pipeline. These tests should still run but not block the build. Mark these tests with a special marker, such as \"@pytest.mark.quarantine\", and create a separate stage in your pipeline to run and report on quarantined tests. Assign an owner for each quarantined test, link it to a ticket, and review the list regularly. Treat quarantine as a temporary solution, not a permanent fix.\n\nFinally, use automatic retries sparingly and as a last resort. While retry plugins can quickly make a pipeline appear green, they often mask the underlying flakiness. If you decide to use retries, limit them to a single retry at most, log every retry occurrence, and keep a record of tests that have been retried. Treat these tests as flaky and prioritize fixing them. By following these steps, you'll create a more reliable pipeline that developers can trust, leading to quicker feedback, fewer manual retries, and a focus on real issues that need investigation.",
  "summary": "A flaky test passes sometimes and fails sometimes, with no change to the code. One flaky test is an annoyance. Twenty of them destroy trust in your pipeline: developers start clicking \"re-run\" by reflex, and real failures slip through because everyone assumes it's \"just flakiness again.\" After many years working on QA and release pipelines, I've found that most flaky tests come from a small…",
  "key_points": [
    "Identify flaky tests by running suite multiple times and tracking results",
    "Replace fixed sleep with explicit waits for user interface/API interactions",
    "Ensure tests are independent by creating isolated data and cleanup after each test"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}